检测、估值与调制理论
2003-1
电子工业
Harry L.Van Trees
697
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本书是一本非常经典的详细介绍检测、估值与调制理论的教学参考书，在美国自1968年首次出版以来，已重印28次。主要内容包括随机过程的表示、检测与估值的分类、信号检测、信号参数的估计、连续波形的估计、线性估计和线性调制理论等。 本书可用做通信与电子、信息与信号处理等专业的高年级本科生、研究生教学参考书。
1 Introduction 1.1 Topical Outline 1.2 Possible Approaches 1.3 Organization2 Classical Detection and Estimation Theory 2.1 Introduction 2.2 Simple Binary Hypothesis Tests Decision Criteria.Performance:Receiver Operating Characteristic 2.3 M Hypotheses 2.4 Estimation Theory Random Parameters:BayesEstimation.Real(Nonrandom)Parameter Estimation.Multiple Parameter Estimation.Summary of Estimation Theory. 2.5 Composite Hypotheses 2.6 The General Gaussian Problem Equal Covariance Matrices.Equal Mean Vectors.Summary. 2.7 Performance Bounds and Approximations 2.8 Summary 2.9 Problems Referecnces3 Representations of Random Processes 3.1 Introduction 3.2 Deterministic Functions:Orthogonal Representations 3.3 Random Process CharacterizationRandom Processes:Conventional Characterizations.Series Representation of Sample Functions of Random Processes.Gaussian Processes. 3.4 Homogeneous Integral Equations and Eigenfunctions Rational Spectra.Bandlimited Spectra.Nonstationary Processes.White Noise Processes.The Optimum Linear Filler.Properties of Eigenfunctions and Eigenvalues. 3.5 Periodic Processes 3.6 Infinite Time Interval:Spectral Decomposition Spectral Decomposition.An Application of Spectral Decomposition:MAP Estimation of a Gaussian Process. 3.7 Vector Random Processes 3.8 Summary 3.9 Problems Referecnces4 Detection of Signals-Estimation of Signal Parameters 4.1 Introduction Models.Format. 4.2 Detection and Estimation in White Gaussian Noise Detection of Signals in Additive White Gaussian Noise.Linear Estimation.Nonlinear Estimation.Summary:Known Signals in White Gaussian Noise 4.3 Detection and Estimation in Nonwhite Gaussian Noise “Whitening”Approach.A Direct Derivation Using the Karhunen-Loeve Expansion.A Direct Derivation Using the Suffcient Statistic.Detection Performance.Estimation.Solution Techniques for Integral Equations.Sensitivity.Known Linear Channels 4.4 Signals with Unwanted Parameters:The Composite Hypothesis Problem Random Phase angles.Random Amplitude and Phase. 4.5 Multiple Channels Formulation.Application. 4.6 Multiple Parameter Estimation Additive White Gaussian Noise Channel.Extensions. 4.7 Summary and Omissions Summary.Topics Omitted. 4.8 Problems Referecnces5 Estimation of Continuous Waveforms 5.1 Introduction 5.2 Derivation of Estimator Equations No-Memory Modulation Systems.Modulation Systems with Memory. 5.3 A Lower Bound on the Mean-Square Estimation Error 5.4 Multidimensional Waveform Estimation Examples of Multidimensional Problems.Problem Formulation.Derivation of Estimator Equations.Lower Bound on the Error Matrix.Colored Noise Estimation. 5.5 Nonrandom Waveform Estimation 5.6 Summary 5.7 Problems Referecnces6 Linear Estimation 6.1 Properties of Optimum Processors 6.2 Realizable Linear Filters:Stationary Processes,Infinite Past:Wiener Filterssolution of Wiener-Hopf Equation.Errors in Optimum Systems.Unrealizable Filters.Closed-Form Error Expressions.Optimum Feedback Systems.Comments. 6.3 Kalman-Bucy Filters Differential Equation Representation of Linear Systems and Random Process Generation.Derivation of Estimator Equations.Applications.Generalizations. 6.4 Linear Modulation:Communications Context DSB-AM:Realizable Demodulation.DSB-AM:Demodulation with delay.Amplitude Modulation:Generalized Carriers.Amplitude Modulation:Single-Sideband Suppressed-Carrier. 6.5 The Fundamental Role of the Optimum Linear Filter 6.6 Comments 6.7 Problems Referecnces7 Discussion 7.1 Summary 7.2 Preview of Part II 7.3 Unexplored Issues ReferencesAppendix:A Typical Course OutlineGlossaryAuthor IndexSubject Index
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经典的书啊，太经典了。。。